Medicine Advances最新文献

Adult patients with patent ductus arteriosus (PDA) have a higher procedural risk during transcatheter PDA closures than infants and children. Limited information is available on transcatheter occlusion in patients with PDA and concomitant comorbidities such as leukemia. We report a case of a 32-year-old man with chronic myeloid leukemia who developed sudden hemoptysis during PDA catheter closure. Immediate treatment involved embolization of a bleeding bronchial artery with polyvinyl alcohol particles. The patient successfully underwent PDA closure and hematopoietic cell transplantation 3 months later. This case emphasizes the importance of evaluating fragile vasculature in patients with comorbid chronic diseases.

The co-existence of artificial intelligence (AI) and human intelligence in solving complex medical issues is inevitable in the future. However, the question remains regarding whether this relationship would continue to be symbiotic and make room for better human-human interactions (the much-yearned patient-physician relationship) in clinical medicine [1].

The evolution of computational power, data science, and machine learning (ML) models is highly perceptible and sometimes unpredictable. Even Gordon Moore (cofounder of Intel®) had to revise his “Moore's law” from ‘the number of transistors on an integrated circuit would double every year’ (1960) to ‘… every 2 years’ (1975) [2]. The same standard holds true for the application of AI in medicine, which ranges from diagnostics, therapeutics (personalized), prognostics, biomedical research (including clinical trials), public health (including pandemic preparedness), and administrative purposes [3]. Some of these possible current applications were barely foreseen and are largely unprecedented (Figure 1).

Through the aforementioned transitions, in addition to the scientific rigor and robustness of AI and ML concepts in medicine, the other considerations are ethical implications, regulatory standards, and legal challenges. Among these, the ethical intricacies surrounding AI in clinical applications are currently being considered upon more keenly and ethical guidelines are being fine-tuned across the world [4-6]. Instances of ethical issues include unfairly incentivizing people of the lower socio-economic strata to contribute personal data to AI development; the chances of cyberattacks on AI technologies, and the ensuing breach in data security and access to sensitive and private information; lack of transparency and explainability regarding how AI-based decisions and recommendations are derived (i.e., how the output is being derived from the input?—“black-box issue”); and overreliance on output from AI-driven technologies (“automation bias”) [5, 7, 8].

Overall, the principles of “transparency”, “justice, fairness, and equity”, “non-maleficence”, “responsibility and accountability”, and “privacy” were found to be common in global guidelines on ethical AI [6, 9].

Additionally, recently, the chatbots (like, ChatGPT and its successor GPT-4) have been the buzzwords in various health-related applications, from academic writing to clearing medical licensing exams, despite their inherent limitations and controversies [10, 11], including language bias [12], regional divide [13], environmental impact [14], and more importantly, compromise on publication ethics [15].

The medical profession is still based on the core principles of love, empathy, and compassion, but this may not always be replicated by ML-based healthcare tools

This letter discusses the clinical utility of ultra-high field magnetic resonance imaging (MRI) at 7 Tesla (7T) for the detection and management of brain metastases. Brain metastases are the most common intracranial tumors and their early detection and management are crucial for improving patient outcomes. Conventional MRI may miss subtle lesions, and 7T provides unprecedented anatomical details with high spatial resolution and improved contrast. It allows the visualization of fine tumor structures and margins, as well as early infiltration and angiogenesis. Techniques such as susceptibility-weighted imaging and quantitative susceptibility mapping exploit the 7T increased sensitivity to magnetic susceptibility effects, facilitating the detection of additional small or hemorrhagic lesions. Advanced methods have also provided novel neuroimaging biomarkers that characterize neuroinflammation and neurovascular changes. While acknowledging limitations including cost and technical challenges, earlier detection and targeted treatment informed by 7T MRI insights could minimize brain metastasis damage and improve patient outcomes.